Abstract:
Glaucoma is the second leading cause of blindness in the world, according to the World
Health Organization, the first being cataracts.Everyone is at risk for glaucoma from
babies to senior citizens. Glaucoma is an eye disease that is characterized by a particular
pattern of progressive damage to the optic nerve that generally begins with a subtle loss
of side vision (peripheral vision). Elevated pressure in the eye is the main factor leading
to glaucomatous damage to the eye (optic) nerve. If not diagnosed and treated, it can
progress to loss of central vision and blindness causing an irreversible damage.Glaucoma
usually causes no symptoms early in its course, at which time it can only be diagnosed
by regular eye examinations. Visual fields are used to diagnose the presence of glaucoma
and monitor its progression .The blind spots created in the patient’s eye by glaucoma
can be seen on the visual field map. This type of test is known as Standard Automated
Perimetry (SAP). There are various types of SAP, but the most commonly used is
Humphrey.In this thesis we use image processing and decision support techniques to
automate the analysis of visual field reports in order to aid ophthalmologists. The
main focus is extract, locate and score quantitatively the glaucomatous damage in each
hemisphere so that the extent of damage may be known. The pattern deviation (PD)
plot printed in the visual field test report contains the significantly depressed points
referred to as probability key symbols (PKS); whose shape, location in ISNT regions
and count give us information about the damage caused by glaucoma to the optic nerve.
Our thesis focuses on the extraction, location region, count and score of these PKSs in
the upper and lower hemispheres of the PD plot hence automating the scoring of the
analysis of visual field tests
